平行学习——机器学习的一个新型理论框架

本文提出了一种新的机器学习理论框架.该框架结合了现有多种机器学习理论框架的优点,并针对如何使用软件定义的人工系统从大数据提取有效数据,如何结合预测学习和集成学习,以及如何利用默顿定律进行指示学习等目前机器学习领域面临的重要问题进行了特别设计.     

平行系统方法与复杂系统的管理和控制

提出平行系统方法的基本思想、概念和运行的基本框架，并讨论了控制系统与平行系统的关系和异同之处，平行系统是控制系统和计算机仿真随着系统复杂程度的增加以及计算技术和分析方法的进一步发展而必然迈上的一个更高的台阶，是弥补很难甚至无法对复杂系统进行精确建模和实验之不足的一种有效手段，也是对复杂系统进行管理和控制的一种可行方式。     

数字孪生与平行系统:发展现状、对比及展望

随着物联网、大数据、人工智能（Artificial intelligence,AI）等技术的发展,针对促进新一代信息技术与制造业深度融合、实现制造物理世界与信息世界交互与共融的需要,数字孪生和平行系统技术成为智能制造和复杂系统管理与控制领域研究的热点.本文对数字孪生和平行系统技术的基本概念、技术内涵、相关应用等进行了研究与总结,对比了两者之间的异同,并分析了两者的发展趋势,预期能够给复杂系统管理与控制领域的研究人员提供一定的参考和借鉴.     

基于ACP方法的平行人力资源管理框架

提出了一种基于数据驱动的企业人力资源管理框架,即平行人力资源管理。以虚实互动的平行思想为核心理念,构建基于人员、岗位、组织画像的人工企业组织,针对人力资源管理中的规划、招聘与配置、培训与开发、绩效管理、薪酬福利管理、劳动关系管理等职能模块,设计解决方案并进行计算实验,对执行结果进行预测、分析、评估,引导实际人力资源组织实施管理决策,形成管理闭环并持续优化。最后,以某集团在人力资源管理领域的改革实践为案例,印证了所提出的平行人力资源管理框架的可行性和科学性。     

城市客运交通枢纽平行系统体系研究

城市客运交通枢纽系统是一个典型的复杂巨系统, 传统的管理方法难以形成具有动态适应能力的有效解决方案. 运用复杂系统研究中的ACP (人工系统、计算机实验、平行执行)方法, 首次给出城市客运交通枢纽平行控制与管理系统研究框架. 以该框架为基础, 首先,分析了人工交通枢纽系统中模型的构成及支撑条件; 进而对人工交通枢纽系统计算实验中涉及的实验场景设计、实验内容设计进行了详细阐述; 最后,对人工交通枢纽系统平行执行内容进行了说明. 该体系框架的提出, 对于城市客运交通枢纽管控水平的提高具有重要指导意义.     

ACP理论的平行执行方式分类研究

针对人工系统、计算实验和平行执行理论(Artificial systems, computational experiments, parallel execution, ACP)中的平行执行方式, 论文给出了不同分类方法,特别是对整体平行执行、局部平行执行和混合平行执行分类方式进行了详细的阐述. 本文以乙烯生产过程为例,论述了整体平行执行、 局部平行执行和混合平行执行分类方法的思想产生来源并进一步阐述了它们的定义、 原理、适用场合和条件,最后,给出了这三种平行执行方式的特点.     

平行图像:图像生成的一个新型理论框架*

为了提高计算机视觉系统的泛化能力,要求利用大规模、多样化、带标注的图像数据集,对视觉模型进行充分的学习与评估.由于从实际场景中获取图像具有局限性,文中提出一种图像生成理论框架,称为平行图像.平行图像的核心单元是软件定义的人工图像系统.从实际场景中获取特定的图像“小数据”,输入人工图像系统,生成大量新的人工图像数据.文中总结平行图像的实现方法,包括图形渲染、图像风格迁移、生成式模型等,并且对比分析人工图像和实际图像的特点,讨论领域适应策略.     

平行博物馆系统：框架、平台、方法及应用

随着基础模型、区块链与元宇宙等技术的迅速发展,新时代博物馆建设面临藏品数字化、服务通融性和智能化管理上的需求和挑战.为此,文中基于平行系统与ACP理论,构建虚实互动的平行博物馆系统,设计平行博物馆系统的体系框架、系统平台与基本方法,旨在实现博物馆的智能化建设和管理.基于数字化采集、场景工程、多模态人机交互、博物馆文博大模型和数字资产管理等子系统构建平行博物馆的系统功能,通过基于场景工程的任务构建、基于人类反馈的知识强化模型以及基于分布式自治组织的数据资源治理提供技术支撑,利用博物馆文物研究与保护、博物馆展览和博物馆运营管理等服务提升博物馆典型业务和服务场景的运行效果.最后,介绍平行博物馆系统的应用场景及案例.     

平行控制: 数据驱动的计算控制方法

本文简述平行控制的理念、概念及基本方法与应用, 主要强调虚实互动的平行扩展方式与同时计算的并行划分方式的不同之处. 平行控制方法是ACP理论在控制领域中的具体应用, 其核心是利用人工系统进行建模和表示、通过计算实验进行分析和评估、最后借助平行执行实现对复杂系统的控制和管理. 这一控制方法是反馈控制, 特别是自适应控制方法向复杂系统问题扩展的自然结果, 是一种迈向数据驱动控制和计算控制的必然且有效途径.     

基于仿真的复杂系统研究

复杂系统是客观存在的,其复杂性和非线性,不仅使其成为一个反直观系统,而且也是进一步提高控制精确度的障碍,势必要深入研究复杂系统.目前,复杂系统的研究方法有二:①涌现,②控制.研究复杂系统大部分都用涌现的方法,而对于控制的方法实际上在人工智能技术问世的时候已经开始使用了,例如专家系统的实践,各种逻辑系统的仿真都是使用这种自下而上的方法完成的.如今,在复杂控制系统仿真得到广泛的应用.基于仿真的复杂系统研究正在掀开复杂系统的面纱,公开复杂系统的奥秘将是21世纪的重要任务之一.     

Parallel Reinforcement Learning: A Framework and Case Study

In this paper, a new machine learning framework is developed for complex system control, called parallel reinforcement learning. To overcome data deficiency of current data-driven algorithms, a parallel system is built to improve complex learning system by self-guidance. Based on the Markov chain (MC) theory, we combine the transfer learning, predictive learning, deep learning and reinforcement learning to tackle the data and action processes and to express the knowledge. Parallel reinforcement learning framework is formulated and several case studies for real-world problems are finally introduced.      

Parallel Control for Continuous-Time Linear Systems: A Case Study

In this paper, a new parallel controller is developed for continuous-time linear systems. The main contribution of the method is to establish a new parallel control law, where both state and control are considered as the input. The structure of the parallel control is provided, and the relationship between the parallel control and traditional feedback controls is presented. Considering the situations that the systems are controllable and incompletely controllable, the properties of the parallel control law are analyzed. The parallel controller design algorithms are given under the conditions that the systems are controllable and incompletely controllable. Finally, numerical simulations are carried out to demonstrate the effectiveness and applicability of the present method.      

Parallel Distance: A New Paradigm of Measurement for Parallel Driving

In this paper, a new paradigm named parallel dis-tance is presented to measure the data information in parallel driving system. As an example, the core variables in the parallel driving system are measured and evaluated in the parallel distance framework. First, the parallel driving 3.0 system included control and management platform, intelligent vehicle platform and remote-control platform is introduced. Then, Markov chain (MC) is utilized to model the transition probability matrix of control commands in these systems. Furthermore, to distinguish the control variables in artificial and physical driving conditions, different distance calculation methods are enumerated to specify the differences between the virtual and real signals. By doing this, the real system can be guided and the virtual system can be im-proved. Finally, simulation results exhibit the merits and multiple applications of the proposed parallel distance framework.      

Parallel Planning: A New Motion Planning Framework for Autonomous Driving

Motion planning is one of the most significant technologies for autonomous driving. To make motion planning models able to learn from the environment and to deal with emergency situations, a new motion planning framework called as "parallel planning" is proposed in this paper. In order to generate sufficient and various training samples, artificial traffic scenes are firstly constructed based on the knowledge from the reality. A deep planning model which combines a convolutional neural network (CNN) with the Long Short-Term Memory module (LSTM) is developed to make planning decisions in an end-toend mode. This model can learn from both real and artificial traffic scenes and imitate the driving style of human drivers. Moreover, a parallel deep reinforcement learning approach is also presented to improve the robustness of planning model and reduce the error rate. To handle emergency situations, a hybrid generative model including a variational auto-encoder (VAE) and a generative adversarial network (GAN) is utilized to learn from virtual emergencies generated in artificial traffic scenes. While an autonomous vehicle is moving, the hybrid generative model generates multiple video clips in parallel, which correspond to different potential emergency scenarios. Simultaneously, the deep planning model makes planning decisions for both virtual and current real scenes. The final planning decision is determined by analysis of real observations. Leveraging the parallel planning approach, the planner is able to make rational decisions without heavy calculation burden when an emergency occurs.      

PAC智能控制沥青强拌设备系统设计

目前沥青搅拌设备中用料称量系统存在计量精度不高,效率低的弊端,本文在PMT360强拌设备结构基础上,采用PACSystemTM RX3i控制器设计出了变累加增量计量。经过现场验证该设计能够减少骨料称量时的冲量,骨料动态计量精度控制在±2.3%之内,PMT360型强拌设备的拌和周期由原来42秒缩短到39秒。在单盘拌和质量不变的情况下,提高了沥青混合料搅拌设备的生产效率。     

Research Progress of Parallel Control and Management

Based on ACP (artificial systems, computational experiments, and parallel execution) methodology, parallel control and management has become a popularly systematic and complete solution for the control and management of complex systems. This paper focuses on summarizing comprehensive review of the research literature of parallel control and management achieved in the recent years including the theoretical framework, core technologies, and the application demonstration. The future research, application directions, and suggestions are also discussed.      

平行应急疏散系统:基本概念、体系框架及其应用

突发事件通常具有难以预测、多成因关联、危害性大及演变复杂等特点,应急情况下如何安全高效疏散人员是应急管控领域的重要研究内容.本文将基于人工系统（Artificial systems,A）、计算实验（Computational experiments,C）、平行执行（Parallel excution,P）方法的平行系统理论引入到应急管控领域,提出平行应急疏散系统（Parallel emergency evacuation systems,PeES）基本概念,构建系统体系框架及集成平台,并介绍人工应急疏散系统、计算实验、平行执行等主要功能模块的基本功能及实现方法.通过PeES能实现虚实应急疏散系统的管理与控制、应急方案的实验与评估以及相关人员的学习与培训.最后,以轨道交通枢纽站火灾场景下的乘客应急疏散为典型应用对平行应急疏散系统进行初步验证.     

Contouring Control for Multi‐Axis Motion Systems With Holonomic Constraints: Theory and Application to a Parallel System

In this paper, the contouring control problem for the constrained multi‐axis motion system is studied. The method of equivalent errors, previously proposed for unconstrained motion systems, is generalized to the system with holonomic constraints. It is shown that the method can be applied to the constrained system provided that the constraints satisfy a proper condition. Because of the constraints, the states in the control law are not completely independent. The unavailable states can be estimated using linear approximation from the constraint equations. As an illustrative example, the proposed method is applied to a parallel motion system with complicated dynamics. A contouring controller is designed using the method of equivalent errors incorporated with integral sliding mode control. Simulation results for contouring circular, elliptic, and square paths verify the effectiveness of the proposed method.     

Discrete Event System Framework for Fault Diagnosis with Measurement Inconsistency: Case Study of Rogue DHCP Attack

Fault detection and diagnosis (FDD) facilitates reliable operation of systems. Various approaches have been proposed for FDD like Analytical redundancy (AR), Principal component analysis (PCA), Discrete event system (DES) model etc., in the literature. Performance of FDD schemes greatly depends on accuracy of the sensors which measure the system parameters. Due to various reasons like faults, communication errors etc., sensors may occasionally miss or report erroneous values of some system parameters to FDD engine, resulting in measurement inconsistency of these parameters. Schemes like AR, PCA etc., have mechanisms to handle measurement inconsistency, however, they are computationally heavy. DES based FDD techniques are widely used because of computational simplicity, but they cannot handle measurement inconsistency efficiently. Existing DES based schemes do not use Measurement inconsistent (MI) parameters for FDD. These parameters are not permanently unmeasurable or erroneous, so ignoring them may lead to weak diagnosis. To address this issue, we propose a Measurement inconsistent discrete event system (MIDES) framework, which uses MI parameters for FDD at the instances they are measured by the sensors. Otherwise, when they are unmeasurable or erroneously reported, the MIDES invokes an estimator diagnoser that predicts the state(s) the system is expected to be in, using the subsequent parameters measured by the other sensors. The efficacy of the proposed method is illustrated using a pumpvalve system. In addition, an MIDES based intrusion detection system has been developed for detection of rogue dynamic host configuration protocol (DHCP) server attack by mapping the attack to a fault in the DES framework.